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Abstract

High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human
motion analysis. However, building a system that can adapt to arbitrary
objects and a wide range of lighting conditions is a challenging problem,
especially if hard real-time constraints apply like in robotics scenarios.
In this work, we introduce a method for learning a discriminative object
tracking system based on the recent structured regression framework for
object localization. Using a kernel function that allows fast evaluation
on the GPU, the resulting system can process video streams at speed of
100 frames per second or more.
Consecutive frames in high speed video sequences are typically very redundant,
and for training an object detection system, it is sufficient to
have training labels from only a subset of all images. We propose an
active learning method that select training examples in a data-driven
way, thereby minimizing the required number of training labeling. Experiments
on realistic data show that the active learning is superior to
previously used methods for dataset subsampling for this task.